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1. An improved Faster R-CNN network for aeroengine fuse fracture detection | |||
Liao Minjie,Bo Lin,Wu Xialing,Liu Qunyang,Wu Wenhong | |||
Computer Science and Technology 13 December 2020 | |||
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Abstract:In order to meet the needs of aeroengine fuse fracture detection in practical application, an improved Faster R-CNN small target detection network is proposed. Firstly, FPN feature graph pyramid is added to improve the extraction ability of small target features, and then ROI Align is used to replace ROI pooling to reduce the loss of feature information of small targets. Experiments on the fuse fracture data set show that the improved detection network is 5.76% higher than Faster R-CNN on mAP. The experimental results show that the improved network is more advanced and has a practical application prospect in aeroengine fuse fracture detection based on computer vision. | |||
TO cite this article:Liao Minjie,Bo Lin,Wu Xialing, et al. An improved Faster R-CNN network for aeroengine fuse fracture detection[OL].[13 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753217 |
2. Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform | |||
Yang Zhihua, Zhang Qian,Yang Lihua | |||
Computer Science and Technology 23 April 2017 | |||
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Abstract:This paper presents a novel method to extract theillumination and rotation invariant features for texture imagesbased on Hilbert-Huang transform. Texture images are usually ofquasi-periodic. It is shown in this paper that the main frequencyof the Hilbert marginal spectrum of a texture image can be used to measure the approximate period effectively and thus can be servedas a good feature for texture classification. This feature isproved to be invariant to uneven illumination. Being modified, itis shown that this feature is also invariant rotation. Experimentshave been conducted to compare the feature with the existing ones.It is shown that the proposed approach outperforms the existingmethods in both recognition rate and robustness to unevenillumination, rotation and noise pollution. | |||
TO cite this article:Yang Zhihua, Zhang Qian,Yang Lihua. Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform[OL].[23 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726832 |
3. Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis | |||
LI Xi-Lian, CHEN Wei, WANG Teng-Jiao | |||
Computer Science and Technology 20 April 2017 | |||
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Abstract:Ideology detection from text plays an important role in identifying the political ideology of politicians who have expressed their beliefs on many issues. Most existing approaches based on bag-of-words features fail to capture semantic information. And other sentence modeling methods are inefficient to extract ideological target context which is significant for identifying the political ideology. In this paper, we propose a target-specific Convolutional and Bi-directional Long Short Term Memory neural network (CB-LSTM) which is expert in intensifying ideological target-related context and learning semantic representations of the text at the same time. We conduct experiments on two commonly used datasets and a well-designed twitter dataset. The experimental results show that the CB-LSTM outperforms the state-of-the-art methods. | |||
TO cite this article:LI Xi-Lian, CHEN Wei, WANG Teng-Jiao. Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis[OL].[20 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726068 |
4. Transfer Learning of Structured Representation for Face Recognition | |||
Chuan-Xian Ren, Dao-Qing Dai | |||
Computer Science and Technology 13 November 2014 | |||
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Abstract:Face recognition under uncontrolled conditions, e.g., complex backgrounds and variable resolutions, is still challenging in image processing and computer vision. Although many methods have been proved well-performed in the controlled settings, they are usually of weak generality across different data sets. Meanwhile, several properties of the source domain, such as background and the size of subjects, play an important role in determining the final classification results. A transferrable representation learning model is proposed in this paper to enhance the recognition performance. To deeply exploit the discriminant information from the source domain and the target domain, the bio-inspired face representation is modeled as structured characterization for the commonality between different domains. The method outputs a grouped boost of the features, and presents a reasonable manner for highlighting and sharing discriminant orientations and scales. Notice that the method can be viewed as a framework, since other feature generation operators and classification metrics can be embedded therein. Experiments on the benchmark databases, including uncontrolled FRGC and LFW show the efficacy of the proposed transfer learning algorithm. | |||
TO cite this article:Chuan-Xian Ren, Dao-Qing Dai. Transfer Learning of Structured Representation for Face Recognition[OL].[13 November 2014] http://en.paper.edu.cn/en_releasepaper/content/4618425 |
5. Optimizing Single-Trial EEG Classi?cation by Stationary Matrix Logistic Regression in Brain-Computer Interface | |||
ZENG Hong,SONG Ai Guo | |||
Computer Science and Technology 29 August 2014 | |||
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Abstract:In addition to the noisy and limited spatial resolution characteristics of the EEG signal, the intrinsic non-stationarity in the EEG data makes the single-trial EEG classi?cation an even more challenging problem in brain-computer interface (BCI). Variations of the signal properties within a session often result in deteriorated classi?cation performance. This is mainly attributed to the reason that the routine feature extraction or classi?cation method does not take the changes in the signal into account. Although several extensions to the standard feature extraction method have been proposed to reduce the sensitivity to non-stationarity in data, they optimizes different objective functions from that of the subsequent classi?cation model, thereby the extracted features may not be optimized for the classi?cation. In this paper, we propose an approach that directly optimizes the classi?er's discriminativity and robustness against non-stationarity in the EEG data with a single optimization paradigm, and show that it can greatly improve the performance, in particular for the subjects who have dif?culty in controlling a BCI. Moreover, the experimental results on two benchmark data sets demonstrate that our approach signi?cantly outperforms the state-of-the-art approaches in reducing classi?cation error rates. | |||
TO cite this article:ZENG Hong,SONG Ai Guo. Optimizing Single-Trial EEG Classi?cation by Stationary Matrix Logistic Regression in Brain-Computer Interface[OL].[29 August 2014] http://en.paper.edu.cn/en_releasepaper/content/4607831 |
6. Sparse representation based on manifold learning | |||
Yang Zheng, Liu Haifeng | |||
Computer Science and Technology 17 December 2013 | |||
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Abstract:As a technology derived from the Human Visual System, sparse coding has attracted a lot of attentions in recent years. It aims to learn sparse coordinates in terms of the basis set, which is given directly or learned from the original data set. Because of the sparsity, the learned sparse representation can be used in further data processing( such as clustering and classifying) efficiently. But the canonical sparse coding methods are all ignored the intrinsic structure of the data. From the perspective of manifold learning, this paper propose a novel sparse coding method, called Sparse Coding based on Manifold learning (MSC). Inspired by LPP, MSC finds a basis set which can be used to represent the intrinsic manifold space of the data set, and then sparse representations will be learned in this space. The most obvious advantage of MSC compared with the algorithms which impose a manifold regularizer to the objective function directly is that MSC is nonparametric. In other words, MSC is more robust. A set of evaluations on real world applications demonstrate the effectiveness of this novel algorithm. | |||
TO cite this article:Yang Zheng, Liu Haifeng. Sparse representation based on manifold learning[OL].[17 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4575298 |
7. Band-reweighed Gabor Kernel Embedding for Face Recognition | |||
Chuan-Xian Ren, Dao-Qing Dai | |||
Computer Science and Technology 07 December 2013 | |||
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Abstract:Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is firstly transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then the features with the largest scores are preserved for saving memory requirement. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition, and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance. | |||
TO cite this article:Chuan-Xian Ren, Dao-Qing Dai. Band-reweighed Gabor Kernel Embedding for Face Recognition[OL].[ 7 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4571407 |
8. Bilinear Lancros components for fast dimensionality reduction and features extraction | |||
Ren Chuanxian,Dai Daoqing | |||
Computer Science and Technology 06 September 2010 | |||
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Abstract:Generalized low rank approximation of matrix (GLRAM) method has received successful application in pattern recognition and machine learning. We use bidirectional Lanczos components to approximate the projective vectors obtained from eigenvalue decomposition in the GLRAM method, where every data points keep the form of two dimensional matrix, instead of one dimensional vectors, thus the time-consuming eigenvalue decomposition procedure is avoided. The method gradually reduces the Frobenius norm based reconstruction error criterion, and lead to the approximation converged to the accurate solution in a success of iterations. Experimental results on face recognition and image classification show that our proposed new method is very efficient and effective. | |||
TO cite this article:Ren Chuanxian,Dai Daoqing. Bilinear Lancros components for fast dimensionality reduction and features extraction[J].pattern recognition ,2010年,43卷,11期,3742 ~ 3752 |
9. Bi-clustering for error-bounded linear patterns in gene expression data | |||
Zeng Tao ,Liu Juan | |||
Computer Science and Technology 13 April 2010 | |||
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Abstract:Bi-clustering is a hard problem in most research fields, especially in the study of gene expression patterns. Except the shifting patterns, along the broadening observations in the study of gene regulatory pathways, scaling patterns or even more general linear patterns are thought to be at least equal importance to investigate. Comparing with MSR only can identify shifting patterns, MMSE is a unified measurement to formal such three types of gene expression patterns. However, MMSE was originally used in node-add bi-clustering framework so that it’s not efficient enough. And in another way, most previous bi-clustering methods only output several first arriving bi-clusters to avoid redundancy in obtained bi-clusters, which can’t completely reveal the distribution of whole potential patterns. So this paper proposes the error-bounded linear gene expression patterns based on MMSE and an effective bi-clustering method (Error-bounded Bi-clustering abbreviate as EB) to enumerate these refined patterns. A widely experiments on 54 controlled synthesized data-sets and 3 yeast cell cycle data-sets strongly support that EB is well matched in strength of currently state-of-the-art clustering/bi-clustering methods according to their biological P-value evaluations; and EB also has the most significant background clusters’ recovery ability than many other bi-clustering methods. | |||
TO cite this article:Zeng Tao ,Liu Juan . Bi-clustering for error-bounded linear patterns in gene expression data[OL].[13 April 2010] http://en.paper.edu.cn/en_releasepaper/content/41845 |
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